Project Manager - Analytics

• Good knowledge on Project Planning and Project Tracking at macro level Strong ability to manage and execute on multiple project timelines and priorities • Experience with Scrum methodology and Agile practices. Should have experience to JIRA • Strong leadership, project management skills, time management, and problem solving skills • Experience in customer handling and should have working in different project phases. • Experience in requirement gather and task mapping. • Experience in effort estimation, project reporting and status tracking. • Should have experience in project level budget control, budget and resource management. Internal project status reporting and customer project status reporting. • Experience in Business Intelligence (BI), Data warehouse (DW) • Clear understanding of Cube, OLAP, OLTP concepts • Hands-on experience of Schema design and data modeling • Should have clear understanding of DFD and ERD • Ability to translate complex data flows/transformations into sequences of ETL implementation tasks • Thorough Knowledge of ETL, data quality, data cleansing, and data blending tools • Familiarity with AWS Data Pipeline a plus • Proven ability to write and tune SQL queries, Understanding of columnar DBs like Redshift and Actian Vector, and conventional RDBMS like SQL Server, MS SQL, MySQL, etc. • Knowledge of one or more OLAP (e.g. Mondrian), reporting and visualization tools (e.g. Tableau, Spotfire, Jaspersoft, QlikView, Looker, DataWatch, Kibana, Apache Zeppelin etc) • Ability to understand domain and gain business knowledge quickly • If Python then, candidate must have used libraries like, Pandas, SciPy, NumPy, Twithon, TwiPy, Anaconda Distribution, etc. • Experience in one more structured DBMS: MySQL, SQL Server, Redshift, Postgres • Experience in one or more: Dynamo DB, Mongo DB, Cassandra • Experience of Ingestion, Processing and Visualization of “Big Data” – More than 500 GB, >1 TB is preferred • Hands-on with Hadoop components like AWS EMR or HDFS, Sqoop, Oozie, HBase, Hive, MapReduce, etc. It is not possible that candidate have all the skills at a time. But these are the basic skills around the Data Engineer position. The candidate should at least be theoretically proficient in all of the above. • Working knowledge on one or more: Kafka, Redis, Spark, Solr, ElasticSearch, Storm, Kinesis Strong analytical skills and good problem solving skills. • Experience in handling large teams, with good interpersonal skills • Self-motivated, team player, action-and-results oriented • Well organized, good communication and reporting skills • Ability to successfully work under tight project deadlines.